


import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__							:						Tuple							     =   logging.get_logger(__name__)


lowerCamelCase__							:						str							     =   {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowerCamelCase__							:						List[str]							     =   {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

lowerCamelCase__							:						str							     =   {"""facebook/blenderbot_small-90M""": 5_1_2}







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[int]:
  snake_case__						= set()
  snake_case__						= word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)				)
    snake_case__						= char

  snake_case__						= set(__lowerCAmelCase				)
  return pairs


class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Tuple	   =       VOCAB_FILES_NAMES
     __lowercase				:							Optional[int]	   =       PRETRAINED_VOCAB_FILES_MAP
     __lowercase				:							List[str]	   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowercase				:							Union[str, Any]	   =       ['input_ids', 'attention_mask']

     def __init__(					self:Dict     ,		_a:List[Any]     ,		_a:List[Any]     ,		_a:Dict="__start__"     ,		_a:Optional[Any]="__end__"     ,		_a:Tuple="__unk__"     ,		_a:int="__null__"     ,		**_a:Optional[Any]     ,		):
       super().__init__(unk_token=_a     ,		bos_token=_a     ,		eos_token=_a     ,		pad_token=_a     ,		**_a   )

       with open(_a     ,		encoding='''utf-8'''   ) as vocab_handle:
         snake_case__						= json.load(_a   )
       snake_case__						= {v: k for k, v in self.encoder.items()}
       with open(_a     ,		encoding='''utf-8'''   ) as merges_handle:
         snake_case__						= merges_handle.read().split('''\n'''   )[1:-1]
       snake_case__						= [tuple(merge.split()   ) for merge in merges]
       snake_case__						= dict(zip(_a     ,		range(len(_a   )   )   )   )
       snake_case__						= {}

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       return len(self.encoder   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       return dict(self.encoder     ,		**self.added_tokens_encoder   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:str   ):
       if token in self.cache:
         return self.cache[token]
       snake_case__						= re.sub('''([.,!?()])'''     ,		r''' \1'''     ,		_a   )
       snake_case__						= re.sub('''(\')'''     ,		r''' \1 '''     ,		_a   )
       snake_case__						= re.sub(r'''\s{2,}'''     ,		''' '''     ,		_a   )
       if "\n" in token:
         snake_case__						= token.replace('''\n'''     ,		''' __newln__'''   )

       snake_case__						= token.split(''' '''   )
       snake_case__						= []
       for token in tokens:
         if not len(_a   ):
           continue

         snake_case__						= token.lower()
         snake_case__						= tuple(_a   )
         snake_case__						= tuple(list(word[:-1]   ) + [word[-1] + '''</w>''']   )
         snake_case__						= get_pairs(_a   )

         if not pairs:
           words.append(_a   )
           continue

         while True:
           snake_case__						= min(_a     ,		key=lambda _a   : self.bpe_ranks.get(_a     ,		float('''inf'''   )   )   )
           if bigram not in self.bpe_ranks:
             break
           snake_case__				,				snake_case__						= bigram
           snake_case__						= []
           snake_case__						= 0

           while i < len(_a   ):
             try:
               snake_case__						= word.index(_a     ,		_a   )
               new_word.extend(word[i:j]   )
               snake_case__						= j
             except ValueError:
               new_word.extend(word[i:]   )
               break

             if word[i] == first and i < len(_a   ) - 1 and word[i + 1] == second:
               new_word.append(first + second   )
               i += 2
             else:
               new_word.append(word[i]   )
               i += 1
           snake_case__						= tuple(_a   )
           snake_case__						= new_word
           if len(_a   ) == 1:
             break
           else:
             snake_case__						= get_pairs(_a   )
         snake_case__						= '''@@ '''.join(_a   )
         snake_case__						= word[:-4]

         snake_case__						= word
         words.append(_a   )
       return " ".join(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:str   ):
       snake_case__						= []

       snake_case__						= re.findall(r'''\S+\n?'''     ,		_a   )

       for token in words:
         split_tokens.extend(list(self.bpe(_a   ).split(''' '''   )   )   )
       return split_tokens

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:str   ):
       snake_case__						= token.lower()
       return self.encoder.get(_a     ,		self.encoder.get(self.unk_token   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:int   ):
       return self.decoder.get(_a     ,		self.unk_token   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:List[str]   ):
       snake_case__						= ''' '''.join(_a   ).replace('''@@ '''     ,		''''''   ).strip()
       return out_string







     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:str     ,		_a:Optional[str] = None   ):
       if not os.path.isdir(_a   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

       with open(_a     ,		'''w'''     ,		encoding='''utf-8'''   ) as f:
         f.write(json.dumps(self.encoder     ,		indent=2     ,		sort_keys=_a     ,		ensure_ascii=_a   ) + '''\n'''   )

       snake_case__						= 0
       with open(_a     ,		'''w'''     ,		encoding='''utf-8'''   ) as writer:
         writer.write('''#version: 0.2\n'''   )
         for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,		key=lambda _a   : kv[1]   ):
           if index != token_index:
             logger.warning(
                 F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                 ''' Please check that the tokenizer is not corrupted!'''   )
             snake_case__						= token_index
           writer.write(''' '''.join(_a   ) + '''\n'''   )
           index += 1

       return vocab_file, merge_file







"""simple docstring"""




from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class        __magic_name__				(			UpperCAmelCase__		,   UpperCAmelCase__		,   UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]





				@register_to_config
				def __init__(			self						,					_a						,					_a						,					_a = None						,					_a = 50_257						,					_a = 1_024						,					_a = 768						,					_a = 12						,					_a = 12						,					_a = None						,					_a = "gelu_new"						,					_a = 0.1						,					_a = 0.1						,					_a = 0.1						,					_a = 1e-5						,					_a = 0.02						,					_a = True						,					_a = True						,					_a = False						,					_a = False						,					):





							"""simple docstring"""
							super().__init__()

							lowerCamelCase      =   prefix_length

							if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
										raise ValueError(
										    f'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
										    f' `n_embd`: {n_embd} are not equal.'					)

							lowerCamelCase      =   prefix_inner_dim
							lowerCamelCase      =   prefix_hidden_dim

							lowerCamelCase      =   (
							    nn.Linear(self.prefix_inner_dim						,					self.prefix_hidden_dim					)
							    if self.prefix_hidden_dim is not None
							    else nn.Identity()
							)
							lowerCamelCase      =   (
							    nn.Linear(self.prefix_hidden_dim						,					_a					) if self.prefix_hidden_dim is not None else nn.Identity()
							)

							lowerCamelCase      =   GPTaConfig(
							    vocab_size=_a						,					n_positions=_a						,					n_embd=_a						,					n_layer=_a						,					n_head=_a						,					n_inner=_a						,					activation_function=_a						,					resid_pdrop=_a						,					embd_pdrop=_a						,					attn_pdrop=_a						,					layer_norm_epsilon=_a						,					initializer_range=_a						,					scale_attn_weights=_a						,					use_cache=_a						,					scale_attn_by_inverse_layer_idx=_a						,					reorder_and_upcast_attn=_a						,					)
							lowerCamelCase      =   GPTaLMHeadModel(_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a = None						,					_a = None						,					):





							"""simple docstring"""
							lowerCamelCase      =   self.transformer.transformer.wte(_a					)
							lowerCamelCase      =   self.encode_prefix(_a					)
							lowerCamelCase      =   self.decode_prefix(_a					)
							lowerCamelCase      =   torch.cat((prefix_embeds, embedding_text)						,					dim=1					)

							if labels is not None:
										lowerCamelCase      =   self.get_dummy_token(input_ids.shape[0]						,					input_ids.device					)
										lowerCamelCase      =   torch.cat((dummy_token, input_ids)						,					dim=1					)
							lowerCamelCase      =   self.transformer(inputs_embeds=_a						,					labels=_a						,					attention_mask=_a					)
							if self.prefix_hidden_dim is not None:
										return out, hidden
							else:
										return out





				def   _lowerCAmelCase							(			self						,					_a						,					_a					):





							"""simple docstring"""
							return torch.zeros(_a						,					self.prefix_length						,					dtype=torch.intaa						,					device=_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return self.encode_prefix(_a					)





				@torch.no_grad()
				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   torch.split(_a						,					1						,					dim=0					)
							lowerCamelCase      =   []
							lowerCamelCase      =   []
							for feature in features:
										lowerCamelCase      =   self.decode_prefix(feature.to(_a					)					)  # back to the clip feature
										# Only support beam search for now
										lowerCamelCase ,  lowerCamelCase      =   self.generate_beam(
										    input_embeds=_a						,					device=_a						,					eos_token_id=_a					)
										generated_tokens.append(output_tokens[0]					)
										generated_seq_lengths.append(seq_lengths[0]					)
							lowerCamelCase      =   torch.stack(_a					)
							lowerCamelCase      =   torch.stack(_a					)
							return generated_tokens, generated_seq_lengths





				@torch.no_grad()
				def   _lowerCAmelCase							(			self						,					_a=None						,					_a=None						,					_a=None						,					_a = 5						,					_a = 67						,					_a = 1.0						,					_a = None						,					):





							"""simple docstring"""
							lowerCamelCase      =   eos_token_id
							lowerCamelCase      =   None
							lowerCamelCase      =   None
							lowerCamelCase      =   torch.ones(_a						,					device=_a						,					dtype=torch.int					)
							lowerCamelCase      =   torch.zeros(_a						,					device=_a						,					dtype=torch.bool					)

							if input_embeds is not None:
										lowerCamelCase      =   input_embeds
							else:
										lowerCamelCase      =   self.transformer.transformer.wte(_a					)

							for i in range(_a					):
										lowerCamelCase      =   self.transformer(inputs_embeds=_a					)
										lowerCamelCase      =   outputs.logits
										lowerCamelCase      =   logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
										lowerCamelCase      =   logits.softmax(-1					).log()

										if scores is None:
													lowerCamelCase ,  lowerCamelCase      =   logits.topk(_a						,					-1					)
													lowerCamelCase      =   generated.expand(_a						,					*generated.shape[1:]					)
													lowerCamelCase ,  lowerCamelCase      =   next_tokens.permute(1						,					0					), scores.squeeze(0					)
													if tokens is None:
																lowerCamelCase      =   next_tokens
													else:
																lowerCamelCase      =   tokens.expand(_a						,					*tokens.shape[1:]					)
																lowerCamelCase      =   torch.cat((tokens, next_tokens)						,					dim=1					)
										else:
													lowerCamelCase      =   -float(np.inf					)
													lowerCamelCase      =   0
													lowerCamelCase      =   scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													lowerCamelCase      =   scores_sum / seq_lengths[:, None]
													lowerCamelCase ,  lowerCamelCase      =   scores_sum_average.view(-1					).topk(_a						,					-1					)
													lowerCamelCase      =   next_tokens // scores_sum.shape[1]
													lowerCamelCase      =   seq_lengths[next_tokens_source]
													lowerCamelCase      =   next_tokens % scores_sum.shape[1]
													lowerCamelCase      =   next_tokens.unsqueeze(1					)
													lowerCamelCase      =   tokens[next_tokens_source]
													lowerCamelCase      =   torch.cat((tokens, next_tokens)						,					dim=1					)
													lowerCamelCase      =   generated[next_tokens_source]
													lowerCamelCase      =   scores_sum_average * seq_lengths
													lowerCamelCase      =   is_stopped[next_tokens_source]

										lowerCamelCase      =   self.transformer.transformer.wte(next_tokens.squeeze()					).view(generated.shape[0]						,					1						,					-1					)
										lowerCamelCase      =   torch.cat((generated, next_token_embed)						,					dim=1					)
										lowerCamelCase      =   is_stopped + next_tokens.eq(_a					).squeeze()
										if is_stopped.all():
													break

							lowerCamelCase      =   scores / seq_lengths
							lowerCamelCase      =   scores.argsort(descending=_a					)
							# tokens tensors are already padded to max_seq_length
							lowerCamelCase      =   [tokens[i] for i in order]
							lowerCamelCase      =   torch.stack(_a						,					dim=0					)
							lowerCamelCase      =   torch.tensor([seq_lengths[i] for i in order]						,					dtype=seq_lengths.dtype					)
							return output_texts, seq_lengths



